Neuroaesthetics in Fashion

Being able to understand and model fashion can have a great impact in
everyday life. In this work we focus on building models that are able to
discover and understand fashion. For this purpose we have created the
Fashion144k dataset, consisting of 144,169 user posts with images and their
associated metadata. We propose the challenging task of identifying the
fashionability of the posts and present a Conditional Random Field model
that is not only able to predict fashionability, but it is also able to give
fashion advice to the users.

In the News

I am keeping a bit of track of where this is being reported. Below is a list of
places that are talking about our research. Please note that these are for
reference only. If you are actually interested in what we do, please read our paper.

In this paper, we analyze the fashion of clothing of a large social website. Our goal is to learn and predict how fashionable a person looks on a photograph and suggest subtle improvements the user could make to improve her/his appeal. We propose a Conditional Random Field model that jointly reasons about several fashionability factors such as the type of outfit and garments the user is wearing, the type of the user, the photograph’s setting (e.g., the scenery behind the user), and the fashionability score. Importantly, our model is able to give rich feedback back to the user, conveying which garments or even scenery she/he should change in order to improve fashionability. We demonstrate that our joint approach significantly outperforms a variety of intelligent baselines. We additionally collected a novel heterogeneous dataset with 144,169 user posts containing diverse image, textual and meta information which can be exploited for our task. We also provide a detailed analysis of the data, showing different outfit trends and fashionability scores across the globe and across a span of 6 years.